The performance results for both evaluation protocols clearly show that the proposed CapsField solution achieves considerably better performance than the non-LF-based deep recognition solutions, presented in the first four rows of Table REF . This improvement is due to the fact that the proposed solution exploits the available spatio-angular information in the LFs for the face expression recognition task. The results also show the superiority of the proposed CapsField solution over the LF-based ones, notably VGG+LSTM [1]} and VGG+BiLSTM+Attention [2]} solutions. As the comparative LF-based and the proposed solutions use the same spatial CNN model, i.e., VGG-16 pre-trained with VGG-Face 2, the performance results reveal that the capsule network better models the richer angular features than the LSTM and BiLSTM recurrent networks used in the benchmarking solutions for the LF expression recognition task.
